Final Report Summary - SYNEURGY (Synergistic modeling of gene-brain couplings with applications in affective neuroscience)
In this project, we aimed to develop and apply multimodal machine learning tools for integration of human brain activity with other modalities. Throughout the project, the developed tools were continually applied and evaluated on neuroimaging data.
The final methodological results included a number of machine learning tools for neuroimaging data, including algorithms for cross-modal synergistic modeling of human genetic effects on brain responses patterns. Utilizing the tools, the Fellow made substantial contributions towards an improved understanding of socio-affective and perceptual brain processing in health and disease. Notable contributions were made in a range of fields, including brain processing of tactile allodynia, multisensory integration, biomarkers of autism and Alzheimer’s disease, and more. In total, the Fellow contributed to 17 conference poster presentations (four selected for talks), one review paper, one book chapter, five journal publications and three conference proceedings papers. Also, one algorithm developed by the Fellow was highlighted by a key blogger in the field (http://mvpa.blogspot.se/2012/09/random-searchlight-averaging.html).
The tools developed in the project provide a new approach for integrating the vast amount of biomedical data, available with state-of-the-art equipment, into the study of the human brain. Such tools are critical for a better understanding of mental illness, and for developing biomarkers of psychiatric conditions. Psychiatric disorders are the single largest contributor to global years lived with disability, and affective disorders alone outrank diabetes, cardiovascular disease and malaria. The Fellow's work during the project has opened new avenues in the effort towards understanding the neurobiology of such illness. In the longer term, this work paves the road towards novel brain-based diagnostics, prognostics and therapeutic interventions in psychiatry.
For dissemination of results to the general public, the Fellow maintains the website at www.syneurgy.eu.
The final methodological results included a number of machine learning tools for neuroimaging data, including algorithms for cross-modal synergistic modeling of human genetic effects on brain responses patterns. Utilizing the tools, the Fellow made substantial contributions towards an improved understanding of socio-affective and perceptual brain processing in health and disease. Notable contributions were made in a range of fields, including brain processing of tactile allodynia, multisensory integration, biomarkers of autism and Alzheimer’s disease, and more. In total, the Fellow contributed to 17 conference poster presentations (four selected for talks), one review paper, one book chapter, five journal publications and three conference proceedings papers. Also, one algorithm developed by the Fellow was highlighted by a key blogger in the field (http://mvpa.blogspot.se/2012/09/random-searchlight-averaging.html).
The tools developed in the project provide a new approach for integrating the vast amount of biomedical data, available with state-of-the-art equipment, into the study of the human brain. Such tools are critical for a better understanding of mental illness, and for developing biomarkers of psychiatric conditions. Psychiatric disorders are the single largest contributor to global years lived with disability, and affective disorders alone outrank diabetes, cardiovascular disease and malaria. The Fellow's work during the project has opened new avenues in the effort towards understanding the neurobiology of such illness. In the longer term, this work paves the road towards novel brain-based diagnostics, prognostics and therapeutic interventions in psychiatry.
For dissemination of results to the general public, the Fellow maintains the website at www.syneurgy.eu.